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Deep learning, self-supervised algorithm for fast and accurate image registration


A deep learning algorithm for fast and accurate, non-rigid image registration that does not require a training data set.



Medical image registration is necessary for aligning multiple clinical images from a single patient as well as for associating medical images from multiple subjects or patients in research studies. Conventional medical image registration can be time consuming and challenging for certain types of images. Therefore, new methods use deep learning techniques like convolutional neural networks, which often employ training image datasets to learn informative image features. These techniques can improve the speed and accuracy of registration. However, in some cases training datasets are unavailable or do not accurately represent the image at hand.



To overcome these challenges, this image registration algorithm uses deep learning methods to identify the appropriate spatial transformations required to align medical images. While other deep learning methods have relied on previously acquired training data, this method directly trains fully convolutional networks to independently estimate spatial transformations.


To account for potentially large deformations between images, a multi-resolution strategy is adopted to jointly learn spatial transformations at different spatial resolutions. Because the registration of pairs of images also serves as a training procedure, the algorithm simultaneously optimizes and learns spatial transformations for the image registration.


 (a) A reference brain space defined by an individual brain image. (b) Mean of brain images of 40 different subjects, registered to the reference brain space using affine spatial transformation. (c) Mean of brain images of 40 different subjects, registered to the reference brain space using the current state-of-the-art in image registration, and (d) the image registration is further enhanced by the deep learning algorithm.



  • Medically necessary imaging that requires fast image registration
  • Large clinical research studies that require accurate image registration


  • Fast image registration (~200 ms per image pair)
  • Improved image alignment compared to current state-of-the-art techniques
  • Multi-resolution registration strategy that accounts for deformations at different scales

Stage of Development:

Developed and tested in a laboratory environment


Intellectual Property:

Provisional patent filed


Desired Partnerships:  

  • License
  • Co-development

Docket # 18-8500

Patent Information:
For Information, Contact:
Jeffrey James
Associate Director, PSOM Licensing Group
University of Pennsylvania
Yong Fan